Occupational Risks for Lung Cancer among Nonsmokers
Bibliographic record
Abstract
We conducted a case-control study in 12 European study centers to evaluate the role of occupational risk factors among nonsmokers. We obtained detailed occupational histories from 650 nonsmoking cases (509 females/141 males) and 1,542 nonsmoking controls (1,011 females/531 males). On the basis of an a priori definition of occupations and industries that are known (list A) or suspected (list B) to be associated with lung carcinogenesis, we calculated odds ratios (ORs) for these occupations, using unconditional logistic regression models and adjusting for sex, age, and center effects. Among nonsmoking men, an excess relative risk was observed among those who had worked in list-A occupations [OR = 1.52; 95% confidence interval (C) = 0.78-2.97] but not in list-B occupations (OR = 1.05; 95%), CI = 0.60-1.83). Among nonsmoking women, there was an elevation of risk for list-A occupations (OR = 1.50; 95% CI = 0.49-4.53), although this estimate was imprecise, given that less than 1% of cases and controls were exposed. Exposure to list-B occupations was associated with an increase in relative risk (OR = 1.69; 95% CI = 1.09-2.63) in females, but not in males. Women who had been laundry workers or dry cleaners had an OR of 1.83 (95% CI = 0.98-3.40). Our findings confirm that certain occupational exposures are associated with an increased risk for lung cancer among both female and male nonsmokers; however, knowledge on occupational lung carcinogens is biased toward agents to which mainly men are exposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".